{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import numpy.random as rng\n",
    "import pandas_datareader.data as web\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_prices(symbol):\n",
    "    start, end = '2007-05-02', '2016-04-11'\n",
    "    data = web.DataReader(symbol, 'google', start, end)\n",
    "    data=pd.DataFrame(data)\n",
    "    prices=data['Close']\n",
    "    prices=prices.astype(float)\n",
    "    return prices\n",
    "\n",
    "def get_returns(prices):\n",
    "        return ((prices-prices.shift(-1))/prices)[:-1]\n",
    "    \n",
    "def get_data(list):\n",
    "    l = []\n",
    "    for symbol in list:\n",
    "        rets = get_returns(get_prices(symbol))\n",
    "        l.append(rets)\n",
    "    return np.array(l).T\n",
    "\n",
    "def sort_data(rets):\n",
    "    ins = []\n",
    "    outs = []\n",
    "    for i in range(len(rets)-100):\n",
    "        ins.append(rets[i:i+100].tolist())\n",
    "        outs.append(rets[i+100])\n",
    "    return np.array(ins), np.array(outs)\n",
    "        \n",
    "symbol_list = ['C', 'GS']\n",
    "rets = get_data(symbol_list)\n",
    "ins, outs = sort_data(rets)\n",
    "ins = ins.transpose([0,2,1]).reshape([-1, len(symbol_list) * 100])\n",
    "div = int(.8 * ins.shape[0])\n",
    "train_ins, train_outs = ins[:div], outs[:div]\n",
    "test_ins, test_outs = ins[div:], outs[div:]\n",
    "\n",
    "#normalize inputs\n",
    "train_ins, test_ins = train_ins/np.std(ins), test_ins/np.std(ins)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "class Model():\n",
    "    def __init__(self, config, training=True):\n",
    "        #CONFIG\n",
    "        symbol_list = self.symbol_list = config.symbol_list\n",
    "        num_samples = self.num_samples =  config.num_samples\n",
    "        input_len = self.input_len =  config.input_len\n",
    "        n_hidden_1 = self.n_hidden_1 =  config.n_hidden_1\n",
    "        n_hidden_2 = self.n_hidden_2 =  config.n_hidden_2 \n",
    "        learning_rate = self.learning_rate = config.learning_rate\n",
    "        \n",
    "        #bucket info\n",
    "        positions = self.positions = tf.constant([-1,0,1])\n",
    "        num_positions = self.num_positions =  3\n",
    "        \n",
    "        #more vars\n",
    "        num_symbols = self.num_symbols =  len(symbol_list)\n",
    "        n_input = self.n_input = num_symbols * input_len\n",
    "        n_classes = self.n_classes = num_positions * num_symbols \n",
    "\n",
    "        \n",
    "        \n",
    "        x =self.x = tf.placeholder(tf.float32, [None, n_input])\n",
    "        y_ =self.y_= tf.placeholder(tf.float32, [None,  num_symbols])\n",
    "\n",
    "        weights = {\n",
    "            'h1': tf.Variable(tf.random_normal([n_input, n_hidden_1])),\n",
    "            'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),\n",
    "            'out': tf.Variable(tf.random_normal([n_hidden_2, n_classes]))\n",
    "        }\n",
    "        biases = {\n",
    "            'b1': tf.Variable(tf.random_normal([n_hidden_1])),\n",
    "            'b2': tf.Variable(tf.random_normal([n_hidden_2])),\n",
    "            'out': tf.Variable(tf.random_normal([n_classes]))\n",
    "        }\n",
    "\n",
    "        \n",
    "        def multilayer_perceptron(x, weights, biases, keep_prob):\n",
    "            # Hidden layer with RELU activation\n",
    "            layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1'])\n",
    "            layer_1 = tf.nn.relu(layer_1)\n",
    "            # Hidden layer with RELU activation\n",
    "            layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2'])\n",
    "            layer_2 = tf.nn.relu(layer_2)\n",
    "            # Output layer with linear activation\n",
    "            out_layer_f = tf.matmul(layer_2, weights['out']) + biases['out']\n",
    "            out_layer = tf.nn.dropout(out_layer_f, keep_prob)                 # DROPOUT LAYER\n",
    "            return out_layer\n",
    "        \n",
    "        if training == True: keep_prob = 0.5  # DROPOUT\n",
    "        else: keep_prob = 1.0                 # NO DROPOUT\n",
    "            \n",
    "        # Construct model\n",
    "        y = multilayer_perceptron(x, weights, biases, keep_prob)\n",
    "\n",
    "\n",
    "\n",
    "        # loop through symbol, taking the columns for each symbol's bucket together\n",
    "        pos = {}\n",
    "        sample_n = {}\n",
    "        sample_mask = {}\n",
    "        symbol_returns = {}\n",
    "        relevant_target_column = {}\n",
    "        for i in range(num_symbols):\n",
    "            # isolate the buckets relevant to the symbol and get a softmax as well\n",
    "            symbol_probs = y[:,i*num_positions:(i+1)*num_positions]\n",
    "            symbol_probs_softmax = tf.nn.softmax(symbol_probs) # softmax[i, j] = exp(logits[i, j]) / sum(exp(logits[i]))\n",
    "            # sample probability to chose our policy's action\n",
    "            sample = tf.multinomial(tf.log(symbol_probs_softmax), num_samples)\n",
    "            for sample_iter in range(num_samples):\n",
    "                sample_n[i*num_samples + sample_iter] = sample[:,sample_iter]\n",
    "                pos[i*num_samples + sample_iter] = tf.reshape(sample_n[i*num_samples + sample_iter], [-1]) - 1\n",
    "                symbol_returns[i*num_samples + sample_iter] = tf.multiply(\n",
    "                                                                    tf.cast(pos[i*num_samples + sample_iter], float32), \n",
    "                                                                     y_[:,i])\n",
    "\n",
    "                sample_mask[i*num_samples + sample_iter] = tf.cast(tf.reshape(tf.one_hot(sample_n[i*num_samples + sample_iter], 3), [-1,3]), float32)\n",
    "                relevant_target_column[i*num_samples + sample_iter] = tf.reduce_sum(\n",
    "                                                            symbol_probs_softmax * sample_mask[i*num_samples + sample_iter],1)\n",
    "        self.pos = pos\n",
    "\n",
    "        # PERFORMANCE METRICS\n",
    "        daily_returns_by_symbol_ = tf.concat(axis=1, values=[tf.reshape(t, [-1,1]) for t in symbol_returns.values()])\n",
    "        daily_returns_by_symbol = tf.transpose(tf.reshape(daily_returns_by_symbol_, [-1,2,num_samples]), [0,2,1]) #[?,5,2]\n",
    "        self.daily_returns = daily_returns = tf.reduce_mean(daily_returns_by_symbol, 2) # [?,5]\n",
    "        \n",
    "        total_return = tf.reduce_prod(daily_returns+1, 0)\n",
    "        z = tf.ones_like(total_return) * -1\n",
    "        self.total_return =total_return= tf.add(total_return, z)\n",
    "        \n",
    "        self.ann_vol = ann_vol = tf.multiply(\n",
    "            tf.sqrt(tf.reduce_mean(tf.pow((daily_returns - tf.reduce_mean(daily_returns, 0)),2),0)) ,\n",
    "            np.sqrt(252)\n",
    "            )\n",
    "        self.sharpe = sharpe = tf.div(total_return, ann_vol)\n",
    "        #Maybe metric slicing later\n",
    "        #segment_ids = tf.ones_like(daily_returns[:,0])\n",
    "        #partial_prod = tf.segment_prod(daily_returns+1, segment_ids)\n",
    "\n",
    "\n",
    "        training_target_cols = tf.concat(axis=1, values=[tf.reshape(t, [-1,1]) for t in relevant_target_column.values()])\n",
    "        ones = tf.ones_like(training_target_cols)\n",
    "        gradient_ = tf.nn.sigmoid_cross_entropy_with_logits(labels=training_target_cols, logits=ones)\n",
    "        gradient = tf.transpose(tf.reshape(gradient_, [-1,2,num_samples]), [0,2,1]) #[?,5,2]\n",
    "\n",
    "        #L2  = tf.contrib.layers.l2_regularizer(0.1)\n",
    "        #t_vars = tf.trainable_variables()\n",
    "        #reg = tf.contrib.layers.apply_regularization(L2, tf.GraphKeys.WEIGHTS)\n",
    "\n",
    "        #cost = tf.multiply(gradient , daily_returns_by_symbol_reshaped)\n",
    "        #cost = tf.multiply(gradient , tf.expand_dims(daily_returns, -1)) #+ reg\n",
    "        cost = tf.multiply(gradient , tf.expand_dims(total_return, -1))\n",
    "#         cost = tf.multiply(gradient , tf.expand_dims(sharpe, -1))\n",
    "\n",
    "        self.optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)\n",
    "        self.costfn = tf.reduce_mean(cost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class SmallConfig(object):\n",
    "    \"\"\"Small config.\"\"\"\n",
    "    symbol_list = ['C', 'GS']\n",
    "    num_samples = 20\n",
    "    input_len = 100\n",
    "    n_hidden_1 = 50 \n",
    "    n_hidden_2 = 50 \n",
    "    learning_rate = 0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0001 cost= 2.28894 total return= 0.412543417 sharpe= 0.240709529\n",
      "Epoch: 0004 cost= 2.1894 total return= 0.403443102 sharpe= 0.235403521\n",
      "Epoch: 0009 cost= 2.30637 total return= 0.414085491 sharpe= 0.241607949\n",
      "Epoch: 0016 cost= 2.30084 total return= 0.413683702 sharpe= 0.241382263\n",
      "Epoch: 0025 cost= 2.19133 total return= 0.403449685 sharpe= 0.235398700\n",
      "Epoch: 0036 cost= 2.20588 total return= 0.404926328 sharpe= 0.236257706\n",
      "Epoch: 0049 cost= 2.32604 total return= 0.415895593 sharpe= 0.242668298\n",
      "Epoch: 0064 cost= 2.31741 total return= 0.415151445 sharpe= 0.242235027\n",
      "Epoch: 0081 cost= 2.26062 total return= 0.410115369 sharpe= 0.239292353\n",
      "Epoch: 0100 cost= 2.27525 total return= 0.411296072 sharpe= 0.239984319\n",
      "Epoch: 0121 cost= 2.30107 total return= 0.413576738 sharpe= 0.241326732\n",
      "Epoch: 0144 cost= 2.22979 total return= 0.407117209 sharpe= 0.237535581\n",
      "Epoch: 0169 cost= 2.25972 total return= 0.409892615 sharpe= 0.239169684\n",
      "Epoch: 0196 cost= 2.34233 total return= 0.417271679 sharpe= 0.243464262\n",
      "Epoch: 0225 cost= 2.27351 total return= 0.410999459 sharpe= 0.239806580\n",
      "Epoch: 0256 cost= 2.31914 total return= 0.415589034 sharpe= 0.242485766\n",
      "Epoch: 0289 cost= 2.12802 total return= 0.397816091 sharpe= 0.232117815\n",
      "Epoch: 0324 cost= 2.24666 total return= 0.408498381 sharpe= 0.238345299\n",
      "Epoch: 0361 cost= 2.25613 total return= 0.409645655 sharpe= 0.239020847\n",
      "Epoch: 0400 cost= 2.2737 total return= 0.411293162 sharpe= 0.239989983\n",
      "Epoch: 0441 cost= 2.19468 total return= 0.403924180 sharpe= 0.235684911\n",
      "Epoch: 0484 cost= 2.28288 total return= 0.412231687 sharpe= 0.240531941\n",
      "Epoch: 0529 cost= 2.22508 total return= 0.406739324 sharpe= 0.237326805\n",
      "Epoch: 0576 cost= 2.30133 total return= 0.413612373 sharpe= 0.241338796\n",
      "Epoch: 0625 cost= 2.15487 total return= 0.399984639 sharpe= 0.233387162\n",
      "Epoch: 0676 cost= 2.21506 total return= 0.405859049 sharpe= 0.236807840\n",
      "Epoch: 0729 cost= 2.25387 total return= 0.409361779 sharpe= 0.238860178\n",
      "Epoch: 0784 cost= 2.21548 total return= 0.405881132 sharpe= 0.236827791\n",
      "Epoch: 0841 cost= 2.243 total return= 0.408317499 sharpe= 0.238239694\n",
      "Epoch: 0900 cost= 2.25471 total return= 0.409596792 sharpe= 0.238994547\n",
      "Epoch: 0961 cost= 2.17321 total return= 0.402169984 sharpe= 0.234657491\n",
      "Epoch: 1024 cost= 2.32493 total return= 0.415850117 sharpe= 0.242644498\n",
      "Epoch: 1089 cost= 2.30981 total return= 0.414417845 sharpe= 0.241802911\n",
      "Epoch: 1156 cost= 2.22072 total return= 0.406300340 sharpe= 0.237063211\n",
      "Epoch: 1225 cost= 2.133 total return= 0.398385345 sharpe= 0.232448589\n",
      "Epoch: 1296 cost= 2.24401 total return= 0.408602156 sharpe= 0.238414471\n",
      "Epoch: 1369 cost= 2.2605 total return= 0.409931899 sharpe= 0.239189362\n",
      "Epoch: 1444 cost= 2.27338 total return= 0.411276667 sharpe= 0.239977454\n",
      "Epoch: 1521 cost= 2.19779 total return= 0.404177753 sharpe= 0.235834460\n",
      "Epoch: 1600 cost= 2.19453 total return= 0.403711313 sharpe= 0.235561493\n",
      "Epoch: 1681 cost= 2.29543 total return= 0.413231721 sharpe= 0.241106577\n",
      "Epoch: 1764 cost= 2.18925 total return= 0.403665258 sharpe= 0.235526970\n",
      "Epoch: 1849 cost= 2.34653 total return= 0.417545755 sharpe= 0.243640275\n",
      "Epoch: 1936 cost= 2.29078 total return= 0.412700057 sharpe= 0.240801863\n"
     ]
    }
   ],
   "source": [
    "if 1==1:\n",
    "    sess = tf.Session()\n",
    "    # initialize variables to random values\n",
    "    with tf.variable_scope(\"model\", reuse=None):\n",
    "        m = Model(config = SmallConfig)\n",
    "    with tf.variable_scope(\"model\", reuse=True):\n",
    "        mvalid = Model(config = SmallConfig, training=False)\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "\n",
    "# run optimizer on entire training data set many times\n",
    "train_size = train_ins.shape[0]\n",
    "for epoch in range(2000):\n",
    "    start = rng.randint(train_size-50)\n",
    "    batch_size = rng.randint(2,75)\n",
    "    end = min(train_size, start+batch_size)\n",
    "    \n",
    "    sess.run(m.optimizer, feed_dict={m.x: train_ins[start:end], m.y_: train_outs[start:end]})#.reshape(1,-1).T})\n",
    "    # every 1000 iterations record progress\n",
    "    if np.sqrt(epoch+1)%1== 0:\n",
    "        t,s, c = sess.run([ mvalid.total_return, mvalid.ann_vol, mvalid.costfn], feed_dict={mvalid.x: train_ins, mvalid.y_: train_outs})#.reshape(1,-1).T})\n",
    "        t = np.mean(t)\n",
    "        t = (1+t)**(1/6) -1\n",
    "        s = np.mean(s)\n",
    "        s = t/s\n",
    "        print(\"Epoch:\", '%04d' % (epoch+1), \"cost=\",c, \"total return=\", \"{:.9f}\".format(t), \n",
    "             \"sharpe=\", \"{:.9f}\".format(s))\n",
    "        #print(t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# in sample results\n",
    "#init = tf.initialize_all_variables()\n",
    "#sess.run(init)\n",
    "d, t = sess.run([mvalid.daily_returns, mvalid.pos[0]], feed_dict={mvalid.x: train_ins, mvalid.y_: train_outs})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Fl1WIEp9Mp1Xl/w47nsyV23Adl8tyX/dXCpdNiWbuHGfHa4ksWCUH/Fx+xxNM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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b071898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# equity curve\n",
    "for i in range(mvalid.num_samples):\n",
    "    plot(np.cumprod(d[:,[i]]+1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x121d9d908>]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SY093UARXAc9FRF8nRw7L+o2IfwP29lDLQNbpO4E1EbE3Il4B1gCLi1VvRPww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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a14f438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#out of sample results\n",
    "d, t = sess.run([mvalid.daily_returns, mvalid.pos[0]], feed_dict={mvalid.x: test_ins, mvalid.y_: test_outs})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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b8axYofSvqSG4bx/1D/9C2fi9/Xbcn31GqHR7t89ZvXMLNZnT2TziXnBWHLfM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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x121e482e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#out of sample results\n",
    "for i in range(5):\n",
    "    plot(np.cumprod(d[:,[i]]+1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x122262320>]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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95piFpYRlThjsCes3I88NTUuVFEskdTjmRXF9Udf0q13zHk0uLil2rfG5n43B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\nQkKChSQYEhISEhIs/D9owsQVlQ8d5AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1220a9080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
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